Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data

Authors: MENARD, CECILE - University Of Edinburgh; ESSERY, RICHARD - University Of Edinburgh; BARR, ALAN - University Of Saskatchewan; BARTLETT, PAUL - Environment And Climate Change Canada; DERRY, JEFF - Department Of Natural Resources; DUMONT, MARIE - University Of Toulouse; FIERZ, CHARLES - Swiss Federal Research Institute Wsl; KIM, HYUNGJUN - University Of Tokyo; KONTU, ANNA - Finnish Meteorological Institute; LEJEUNNE, YVES - University Of Toulouse; Marks, Daniel; NIWANO, MASASHI - National Meteorological Center; RALEIGH, MARK - University Of Colorado; WANG, LIBO - Environment And Climate Change Canada; WEVER, NANDER - National Oceanic & Atmospheric Administration (NOAA)

Submitted to: Earth System Science Data
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/26/2019
Publication Date: 6/17/2019
Citation: Menard, C.B., Essery, R., Barr, A., Bartlett, P., Derry, J., Dumont, M., Fierz, C., Kim, H., Kontu, A., Lejeunne, Y., Marks, D., Niwano, M., Raleigh, M., Wang, L., Wever, N. 2019. Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data. Earth System Science Data. 11:865-880. https://doi.org/10.5194/essd-11-865-2019.
DOI: https://doi.org/10.5194/essd-11-865-2019

Interpretive Summary: This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions' modelling at 10 sites. The long-term datasets (one maritime, one arctic, three boreal, and five mid-latitude alpine) are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project. Periods covered by the in situ data vary between 7 and 20 years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature, and surface temperature) available at varying temporal intervals. Thirty-year (1980–2010) time series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to 1'h time steps and bias-corrected. Although the correction was applied to all sites, it was most important for mountain sites hundreds of metres higher than the grid elevations and for which uncorrected air temperatures were too high and snowfall amounts too low. The discussion considers the importance of data sharing to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The Supplement provides information on instrumentation, an estimate of the percentages of missing values, and gap-filling methods at each site. It is hoped that these datasets will be used as benchmarks for future model development and that their ease of use and availability will help model developers quantify model uncertainties and reduce model errors. The data are published in the repository PANGAEA and are available at https://doi.pangaea.de/10.1594/PANGAEA.897575.

Technical Abstract: This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions' modelling at 10 sites. The long-term datasets (one maritime, one arctic, three boreal, and five mid-latitude alpine) are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project. Periods covered by the in situ data vary between 7 and 20 years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature, and surface temperature) available at varying temporal intervals. Thirty-year (1980–2010) time series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to 1'h time steps and bias-corrected. Although the correction was applied to all sites, it was most important for mountain sites hundreds of metres higher than the grid elevations and for which uncorrected air temperatures were too high and snowfall amounts too low. The discussion considers the importance of data sharing to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The Supplement provides information on instrumentation, an estimate of the percentages of missing values, and gap-filling methods at each site. It is hoped that these datasets will be used as benchmarks for future model development and that their ease of use and availability will help model developers quantify model uncertainties and reduce model errors. The data are published in the repository PANGAEA and are available at https://doi.pangaea.de/10.1594/PANGAEA.897575.